{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "979d141e",
   "metadata": {},
   "source": [
    "## Backtesting a Momentum Factor Strategy with Zipline Reloaded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce97b7c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install logbook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4669abfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a5d74e34",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pandas_datareader as web\n",
    "from IPython.display import Markdown, display\n",
    "from zipline import run_algorithm\n",
    "from zipline.api import (\n",
    "    attach_pipeline,\n",
    "    calendars,\n",
    "    date_rules,\n",
    "    get_open_orders,\n",
    "    order_target_percent,\n",
    "    pipeline_output,\n",
    "    record,\n",
    "    schedule_function,\n",
    "    set_commission,\n",
    "    set_slippage,\n",
    "    time_rules,\n",
    ")\n",
    "from zipline.pipeline import Pipeline\n",
    "from zipline.pipeline.data import USEquityPricing\n",
    "from zipline.pipeline.factors import AverageDollarVolume, CustomFactor, Returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "763852d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6ddf778c",
   "metadata": {},
   "outputs": [],
   "source": [
    "N_LONGS = N_SHORTS = 50"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6e133cb",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "Define a custom momentum factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "47b395ea",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "class MomentumFactor(CustomFactor):\n",
    "    inputs = [USEquityPricing.close, Returns(window_length=126)]\n",
    "    window_length = 252\n",
    "\n",
    "    def compute(self, today, assets, out, prices, returns):\n",
    "        out[:] = (\n",
    "            (prices[-21] - prices[-252]) / prices[-252]\n",
    "            - (prices[-1] - prices[-21]) / prices[-21]\n",
    "        ) / np.nanstd(returns, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccb0a076",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "Define the pipeline for factors and securities selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "472e123e",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "def make_pipeline():\n",
    "    momentum = MomentumFactor()\n",
    "    dollar_volume = AverageDollarVolume(window_length=30)\n",
    "    return Pipeline(\n",
    "        columns={\n",
    "            \"factor\": momentum,\n",
    "            \"longs\": momentum.top(N_LONGS),\n",
    "            \"shorts\": momentum.bottom(N_SHORTS),\n",
    "            \"ranking\": momentum.rank(),\n",
    "        },\n",
    "        screen=dollar_volume.top(100),\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "376be1dc",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "Define the function to be called before the trading starts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5051ffc2",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "def before_trading_start(context, data):\n",
    "    context.factor_data = pipeline_output(\"factor_pipeline\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "270ddb0e",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "Define the initialize function to set up the algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "74957b99",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "def initialize(context):\n",
    "    attach_pipeline(make_pipeline(), \"factor_pipeline\")\n",
    "    schedule_function(\n",
    "        rebalance,\n",
    "        date_rules.week_start(),\n",
    "        time_rules.market_open(),\n",
    "        calendar=calendars.US_EQUITIES,\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4188ab22",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "Define the rebalance function to execute trades"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "16bc903f",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "def rebalance(context, data):\n",
    "    factor_data = context.factor_data\n",
    "    record(factor_data=factor_data.ranking)\n",
    "\n",
    "    assets = factor_data.index\n",
    "    record(prices=data.current(assets, \"price\"))\n",
    "\n",
    "    longs = assets[factor_data.longs]\n",
    "    shorts = assets[factor_data.shorts]\n",
    "    divest = set(context.portfolio.positions.keys()) - set(longs.union(shorts))\n",
    "\n",
    "    exec_trades(data, assets=divest, target_percent=0)\n",
    "    exec_trades(data, assets=longs, target_percent=1 / N_LONGS)\n",
    "    exec_trades(data, assets=shorts, target_percent=-1 / N_SHORTS)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09dd1512",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "Define the function to execute trades"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c4539b48",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "def exec_trades(data, assets, target_percent):\n",
    "    for asset in assets:\n",
    "        if data.can_trade(asset) and not get_open_orders(asset):\n",
    "            order_target_percent(asset, target_percent)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb9dbbc6",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "Define the analyze function to plot performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "42e9b5a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def analyze(context, perf):\n",
    "    perf.portfolio_value.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "001de195",
   "metadata": {},
   "source": [
    "Define the start and end dates for the backtest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5b630eae",
   "metadata": {},
   "outputs": [],
   "source": [
    "start = pd.Timestamp(\"2016\")\n",
    "end = pd.Timestamp(\"2018\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "720a98a0",
   "metadata": {},
   "source": [
    "Fetch the benchmark returns (S&P 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b8a02765",
   "metadata": {},
   "outputs": [],
   "source": [
    "sp500 = web.DataReader(\"SP500\", \"fred\", start, end).SP500\n",
    "benchmark_returns = sp500.pct_change()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52e4b971",
   "metadata": {},
   "source": [
    "Run the algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cd037d31",
   "metadata": {},
   "outputs": [],
   "source": [
    "perf = run_algorithm(\n",
    "    start=start,\n",
    "    end=end,\n",
    "    initialize=initialize,\n",
    "    before_trading_start=before_trading_start,\n",
    "    capital_base=100_000,\n",
    "    benchmark_returns=benchmark_returns,\n",
    "    bundle=\"quandl\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "828d4954",
   "metadata": {},
   "source": [
    "Display performance info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "303ad0a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 503 entries, 2016-01-04 21:00:00+00:00 to 2017-12-29 21:00:00+00:00\n",
      "Data columns (total 39 columns):\n",
      " #   Column                   Non-Null Count  Dtype              \n",
      "---  ------                   --------------  -----              \n",
      " 0   period_open              503 non-null    datetime64[ns, UTC]\n",
      " 1   period_close             503 non-null    datetime64[ns, UTC]\n",
      " 2   longs_count              503 non-null    int64              \n",
      " 3   ending_value             503 non-null    float64            \n",
      " 4   long_exposure            503 non-null    float64            \n",
      " 5   pnl                      503 non-null    float64            \n",
      " 6   capital_used             503 non-null    float64            \n",
      " 7   short_exposure           503 non-null    float64            \n",
      " 8   orders                   503 non-null    object             \n",
      " 9   net_leverage             503 non-null    float64            \n",
      " 10  gross_leverage           503 non-null    float64            \n",
      " 11  ending_cash              503 non-null    float64            \n",
      " 12  short_value              503 non-null    float64            \n",
      " 13  positions                503 non-null    object             \n",
      " 14  returns                  503 non-null    float64            \n",
      " 15  transactions             503 non-null    object             \n",
      " 16  portfolio_value          503 non-null    float64            \n",
      " 17  long_value               503 non-null    float64            \n",
      " 18  starting_exposure        503 non-null    float64            \n",
      " 19  ending_exposure          503 non-null    float64            \n",
      " 20  starting_value           503 non-null    float64            \n",
      " 21  shorts_count             503 non-null    int64              \n",
      " 22  starting_cash            503 non-null    float64            \n",
      " 23  factor_data              503 non-null    object             \n",
      " 24  prices                   503 non-null    object             \n",
      " 25  trading_days             503 non-null    int64              \n",
      " 26  algo_volatility          502 non-null    float64            \n",
      " 27  sharpe                   502 non-null    float64            \n",
      " 28  period_label             503 non-null    object             \n",
      " 29  treasury_period_return   503 non-null    float64            \n",
      " 30  max_drawdown             503 non-null    float64            \n",
      " 31  alpha                    502 non-null    float64            \n",
      " 32  beta                     502 non-null    float64            \n",
      " 33  excess_return            503 non-null    float64            \n",
      " 34  sortino                  502 non-null    float64            \n",
      " 35  algorithm_period_return  503 non-null    float64            \n",
      " 36  benchmark_period_return  503 non-null    float64            \n",
      " 37  benchmark_volatility     502 non-null    float64            \n",
      " 38  max_leverage             503 non-null    float64            \n",
      "dtypes: datetime64[ns, UTC](2), float64(28), int64(3), object(6)\n",
      "memory usage: 157.2+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(perf.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfd7fb87",
   "metadata": {},
   "source": [
    "Plot cumulative returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0431cf9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Cumulative returns'}>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "perf.portfolio_value.plot(title=\"Cumulative returns\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65145a66",
   "metadata": {},
   "source": [
    "Plot histogram of returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "549139b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "perf.returns.hist(bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8f737ad",
   "metadata": {},
   "source": [
    "Plot Sharpe ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3578bf6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
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    "**Jason Strimpel** is the founder of <a href='https://pyquantnews.com/'>PyQuant News</a> and co-founder of <a href='https://www.tradeblotter.io/'>Trade Blotter</a>. His career in algorithmic trading spans 20+ years. He previously traded for a Chicago-based hedge fund, was a risk manager at JPMorgan, and managed production risk technology for an energy derivatives trading firm in London. In Singapore, he served as APAC CIO for an agricultural trading firm and built the data science team for a global metals trading firm. Jason holds degrees in Finance and Economics and a Master's in Quantitative Finance from the Illinois Institute of Technology. His career spans America, Europe, and Asia. He shares his expertise through the <a href='https://pyquantnews.com/subscribe-to-the-pyquant-newsletter/'>PyQuant Newsletter</a>, social media, and has taught over 1,000+ algorithmic trading with Python in his popular course **<a href='https://gettingstartedwithpythonforquantfinance.com/'>Getting Started With Python for Quant Finance</a>**. All code is for educational purposes only. Nothing provided here is financial advise. Use at your own risk."
   ]
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